Cargando…

Autism risk classification using placental chorionic surface vascular network features

BACKGROUND: Autism Spectrum Disorder (ASD) is one of the fastest-growing developmental disorders in the United States. It was hypothesized that variations in the placental chorionic surface vascular network (PCSVN) structure may reflect both the overall effects of genetic and environmentally regulat...

Descripción completa

Detalles Bibliográficos
Autores principales: Chang, Jen-Mei, Zeng, Hui, Han, Ruxu, Chang, Ya-Mei, Shah, Ruchit, Salafia, Carolyn M., Newschaffer, Craig, Miller, Richard K., Katzman, Philip, Moye, Jack, Fallin, Margaret, Walker, Cheryl K., Croen, Lisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719902/
https://www.ncbi.nlm.nih.gov/pubmed/29212472
http://dx.doi.org/10.1186/s12911-017-0564-8
_version_ 1783284575986778112
author Chang, Jen-Mei
Zeng, Hui
Han, Ruxu
Chang, Ya-Mei
Shah, Ruchit
Salafia, Carolyn M.
Newschaffer, Craig
Miller, Richard K.
Katzman, Philip
Moye, Jack
Fallin, Margaret
Walker, Cheryl K.
Croen, Lisa
author_facet Chang, Jen-Mei
Zeng, Hui
Han, Ruxu
Chang, Ya-Mei
Shah, Ruchit
Salafia, Carolyn M.
Newschaffer, Craig
Miller, Richard K.
Katzman, Philip
Moye, Jack
Fallin, Margaret
Walker, Cheryl K.
Croen, Lisa
author_sort Chang, Jen-Mei
collection PubMed
description BACKGROUND: Autism Spectrum Disorder (ASD) is one of the fastest-growing developmental disorders in the United States. It was hypothesized that variations in the placental chorionic surface vascular network (PCSVN) structure may reflect both the overall effects of genetic and environmentally regulated variations in branching morphogenesis within the conceptus and the fetus’ vital organs. This paper provides sound evidences to support the study of ASD risks with PCSVN through a combination of feature-selection and classification algorithms. METHODS: Twenty eight arterial and 8 shape-based PCSVN attributes from a high-risk ASD cohort of 89 placentas and a population-based cohort of 201 placentas were examined for ranked relevance using a modified version of the random forest algorithm, called the Boruta method. Principal component analysis (PCA) was applied to isolate principal effects of arterial growth on the fetal surface of the placenta. Linear discriminant analysis (LDA) with a 10-fold cross validation was performed to establish error statistics. RESULTS: The Boruta method selected 15 arterial attributes as relevant, implying the difference in high and low ASD risk can be explained by the arterial features alone. The five principal features obtained through PCA, which accounted for about 88% of the data variability, indicated that PCSVNs associated with placentas of high-risk ASD pregnancies generally had fewer branch points, thicker and less tortuous arteries, better extension to the surface boundary, and smaller branch angles than their population-based counterparts. CONCLUSION: We developed a set of methods to explain major PCSVN differences between placentas associated with high risk ASD pregnancies and those selected from the general population. The research paradigm presented can be generalized to study connections between PCSVN features and other maternal and fetal outcomes such as gestational diabetes and hypertension.
format Online
Article
Text
id pubmed-5719902
institution National Center for Biotechnology Information
language English
publishDate 2017
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-57199022017-12-11 Autism risk classification using placental chorionic surface vascular network features Chang, Jen-Mei Zeng, Hui Han, Ruxu Chang, Ya-Mei Shah, Ruchit Salafia, Carolyn M. Newschaffer, Craig Miller, Richard K. Katzman, Philip Moye, Jack Fallin, Margaret Walker, Cheryl K. Croen, Lisa BMC Med Inform Decis Mak Research Article BACKGROUND: Autism Spectrum Disorder (ASD) is one of the fastest-growing developmental disorders in the United States. It was hypothesized that variations in the placental chorionic surface vascular network (PCSVN) structure may reflect both the overall effects of genetic and environmentally regulated variations in branching morphogenesis within the conceptus and the fetus’ vital organs. This paper provides sound evidences to support the study of ASD risks with PCSVN through a combination of feature-selection and classification algorithms. METHODS: Twenty eight arterial and 8 shape-based PCSVN attributes from a high-risk ASD cohort of 89 placentas and a population-based cohort of 201 placentas were examined for ranked relevance using a modified version of the random forest algorithm, called the Boruta method. Principal component analysis (PCA) was applied to isolate principal effects of arterial growth on the fetal surface of the placenta. Linear discriminant analysis (LDA) with a 10-fold cross validation was performed to establish error statistics. RESULTS: The Boruta method selected 15 arterial attributes as relevant, implying the difference in high and low ASD risk can be explained by the arterial features alone. The five principal features obtained through PCA, which accounted for about 88% of the data variability, indicated that PCSVNs associated with placentas of high-risk ASD pregnancies generally had fewer branch points, thicker and less tortuous arteries, better extension to the surface boundary, and smaller branch angles than their population-based counterparts. CONCLUSION: We developed a set of methods to explain major PCSVN differences between placentas associated with high risk ASD pregnancies and those selected from the general population. The research paradigm presented can be generalized to study connections between PCSVN features and other maternal and fetal outcomes such as gestational diabetes and hypertension. BioMed Central 2017-12-06 /pmc/articles/PMC5719902/ /pubmed/29212472 http://dx.doi.org/10.1186/s12911-017-0564-8 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Chang, Jen-Mei
Zeng, Hui
Han, Ruxu
Chang, Ya-Mei
Shah, Ruchit
Salafia, Carolyn M.
Newschaffer, Craig
Miller, Richard K.
Katzman, Philip
Moye, Jack
Fallin, Margaret
Walker, Cheryl K.
Croen, Lisa
Autism risk classification using placental chorionic surface vascular network features
title Autism risk classification using placental chorionic surface vascular network features
title_full Autism risk classification using placental chorionic surface vascular network features
title_fullStr Autism risk classification using placental chorionic surface vascular network features
title_full_unstemmed Autism risk classification using placental chorionic surface vascular network features
title_short Autism risk classification using placental chorionic surface vascular network features
title_sort autism risk classification using placental chorionic surface vascular network features
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719902/
https://www.ncbi.nlm.nih.gov/pubmed/29212472
http://dx.doi.org/10.1186/s12911-017-0564-8
work_keys_str_mv AT changjenmei autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT zenghui autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT hanruxu autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT changyamei autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT shahruchit autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT salafiacarolynm autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT newschaffercraig autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT millerrichardk autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT katzmanphilip autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT moyejack autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT fallinmargaret autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT walkercherylk autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures
AT croenlisa autismriskclassificationusingplacentalchorionicsurfacevascularnetworkfeatures